options(warn=-1)library(tidyr)
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terr = read.csv('~/Downloads/globalterrorismdb_0617dist.csv', check.names = FALSE, header = TRUE, stringsAsFactors = FALSE)terr=rename(terr, id=eventid, year=iyear, nation=country_txt,
Region=region_txt, attack=attacktype1_txt,
target=targtype1_txt, weapon=weaptype1_txt,
Killed=nkill, wounded=nwound)We clean the data
terr$Killed=as.integer(terr$Killed)
terr$wounded=as.integer(terr$wounded)
terr$Killed[which(is.na(terr$Killed))] = 0
terr$wounded[which(is.na(terr$wounded))] = 0
terr$casualties=as.integer(terr$Killed+terr$wounded)
terr$nation[terr$nation=="United States"] <- "USA"
terr$nation[terr$nation=="United Kingdom"] <- "UK"
terr$nation[terr$nation=="People's Republic of the Congo"] <- "Republic of Congo"
terr$nation[terr$nation=="Bosnia-Herzegovina"] <- "Bosnia and Herzegovina"
terr$nation[terr$nation=="Slovak Republic"] <- "Slovakia"global_t <-
terr %>%
group_by(year,nation,Region) %>%
summarize(Total=n())
global_y <-
global_t %>%
group_by(year) %>%
summarize(Total=sum(Total))
global_attacks <-
global_t %>%
group_by(nation) %>%
summarize(Total=sum(Total)) %>%
arrange(desc(Total))
attach(global_attacks)
global_n <- global_attacks[order(-Total),]
detach(global_attacks)Let’s look at the number of terrorist attacks with the passage of time.
gy <- global_y %>%
ggplot(mapping=aes(year,Total))+
geom_line(color="red")+
theme(legend.position="none", panel.background = NULL, axis.text.x = element_text(angle=45, vjust = 1))+
labs(x="Year", y="Number of attacks", title="Number of global attacks over years")
ggplotly(gy, width = 800, height=480)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
global_kills_years <-
terr %>%
group_by(year) %>%
summarize(killed=sum(Killed))
global_wound_years <-
terr %>%
group_by(year) %>%
summarize(wounded=sum(wounded))
globe <-
global_kills_years %>%
inner_join(global_wound_years, by="year") %>%
inner_join(global_y)## Joining, by = "year"
df <- melt(globe, "year")
df=rename(df, effect=variable)
gky <- df %>%
ggplot(mapping=aes(x=year,y=value, color=effect))+
geom_line()+
theme(panel.background = NULL, axis.text.x = element_text(angle=45, vjust = 1))+
labs(x="Year", y="Count", title="Number of people killed/wounded over years against attacks")
ggplotly(gky, width = 800, height=450)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
High peaks can be seen in the people killed in the year 1984. In 2001, even though there was a fall in no. of terrorist attacks, the number of casualties were on a peak. Number of casualties suddenly started rising from 2011 to 2015.
#get weapon most used in each nation
terr$casualties=as.integer(terr$Killed+terr$wounded)
terr$casualties[which(is.na(terr$casualties))]=0g_max_cas <- terr%>%
top_n(10, casualties) %>%
ggplot(mapping=aes(x=reorder(target1, -casualties), y=casualties, fill=target1)) +
geom_bar(stat = 'identity')+
theme(legend.position="none", panel.background = NULL, axis.text.x = element_text(angle=50, vjust = 1))+
labs(x="Target of attack", y="Number of casulaties", title="Terrorist attacks with most casualties")
ggplotly(g_max_cas)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
gname_max_cas <- terr[c('gname', 'casualties')]%>%
filter(gname!='Unknown') %>%
group_by(gname) %>%
summarize(Total=n())
g <- gname_max_cas %>%
top_n(40, Total) %>%
ggplot(mapping=aes(x=reorder(gname, -Total), y=Total, fill=gname)) +
geom_bar(stat = 'identity')+
theme(legend.position="none", panel.background = NULL, axis.text.x = element_text(angle=50, vjust = 1))+
labs(x="Terrorist group", y="Number of casulaties", title="Terrorist groups leading to most casualties")
ggplotly(g, width = 800, height = 450)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
Let’s look at the 40 countries with maximum number of terrorist attacks, and 40 countries with least number of terrorist attacks
g2 <- global_n%>%
top_n(40) %>%
ggplot(mapping=aes(x=reorder(nation, -Total),y=Total,fill=nation)) +
geom_bar(stat='identity')+
theme(legend.position="none", panel.background = NULL, axis.text.x = element_text(angle=90, vjust = 1))+
labs(x="Countries", y="Number of attacks", title="Countries with most number of terrorist attacks")## Selecting by Total
ggplotly(g2, width = 800, height=450)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
g2 <- global_n%>%
top_n(-40)%>%
ggplot(mapping=aes(x=reorder(nation, Total),y=Total,fill=nation)) +
geom_bar(stat='identity')+
theme(legend.position="none", panel.background = NULL, axis.text.x = element_text(angle=90, vjust = 1))+
labs(x="Countries", y="Number of attacks", title="Countries with least number of terrorist attacks")## Selecting by Total
ggplotly(g2, width = 800, height=450)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
We’ll take a look at relationship of some parameters. These relations, however, do not directly imply causation. Further analysis should be done for implying causation.
g1 <- terr %>%
ggplot(aes(x = Region, y = casualties, fill=Region)) +
geom_boxplot() +
theme(legend.position = "none", axis.text.x = element_text(angle=45))
ylim1 = boxplot.stats(terr$casualties)$stats[c(1,5)]
g2 <- g1+coord_cartesian(ylim = ylim1*1.05)
ggplotly(g2)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
We can see that Middle East & North Africa has a higher median number of casualties(2) than other regions, which same as that for Sub-Saharan Africa. The least variant region in terms of number of casualties is North America. However, it has lot many outliers, with the 9/11 attacks resulting in most number of casualties(8749).
g1 <- terr %>%
ggplot(aes(x = attack, y = casualties, fill=attack)) +
geom_boxplot() +
theme(legend.position = "none", axis.text.x = element_text(angle=45))
ylim1 = boxplot.stats(terr$casualties)$stats[c(1,5)]
g2 <- g1+coord_cartesian(ylim = ylim1*1.05)
ggplotly(g2, height = 500)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
Leaving out the unknown attack types, no. of casualties are most highly variant in case of bombings/explosions. Hijacking and Hostage Taking(s) have low variance in no. of casualties, with outliers as high as 8749 in case of hijacking.
g1 <- terr %>%
ggplot(aes(x = weapon, y = casualties, fill=weapon)) +
geom_boxplot() +
theme(legend.position = "none", axis.text.x = element_text(angle=45))
ylim1 = boxplot.stats(terr$casualties)$stats[c(1,5)]
g2 <- g1+coord_cartesian(ylim = ylim1*1.05)
ggplotly(g2, height = 500)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
The no. of casualties because of chemical weapons has been highly variant, with 25% of chemical attacks resultin casualties between 50 to 5513. There hasn’t been any casualty because of radilogical weapons.
g1 <- terr %>%
ggplot(aes(x = target, y = casualties, fill=target)) +
geom_boxplot() +
theme(legend.position = "none", axis.text.x = element_text(angle=45))
ylim1 = boxplot.stats(terr$casualties)$stats[c(1,5)]
g2 <- g1+coord_cartesian(ylim = ylim1*1.05)
ggplotly(g2, height = 500)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
Casualties related to transporation, military, and Private citizens & property have high variance. Attacks on Police, and non-state militia generally lead to more than 1 casualty, with as many as 11 casualties in some cases.
g1 <- terr %>%
filter(INT_ANY!=-9)%>%
ggplot(aes(x = factor(INT_ANY), y = casualties, fill=INT_ANY)) +
geom_boxplot() +
theme(legend.position = "none", axis.text.x = element_text(angle=45))
ylim1 = boxplot.stats(terr$casualties)$stats[c(1,5)]
g2 <- g1+coord_cartesian(ylim = ylim1*1.05)
ggplotly(g2, height = 500)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
Domestic attacks usually had higher number of casualties as compared to attacks that were international on any level, i.e. logistics, ideological, or miscellaneous reason.
#getting proportions across attack and region
t = table(data.frame(terr$attack,terr$Region));
prop.table(t,2)*100## terr.Region
## terr.attack Australasia & Oceania
## Armed Assault 18.5606061
## Assassination 11.3636364
## Bombing/Explosion 27.6515152
## Facility/Infrastructure Attack 25.3787879
## Hijacking 1.1363636
## Hostage Taking (Barricade Incident) 1.8939394
## Hostage Taking (Kidnapping) 4.1666667
## Unarmed Assault 3.7878788
## Unknown 6.0606061
## terr.Region
## terr.attack Central America & Caribbean
## Armed Assault 42.1663443
## Assassination 12.1179884
## Bombing/Explosion 31.3249516
## Facility/Infrastructure Attack 3.8781431
## Hijacking 0.2514507
## Hostage Taking (Barricade Incident) 1.8085106
## Hostage Taking (Kidnapping) 4.8452611
## Unarmed Assault 0.1837524
## Unknown 3.4235977
## terr.Region
## terr.attack Central Asia East Asia
## Armed Assault 20.7581227 14.3576826
## Assassination 20.5776173 6.9269521
## Bombing/Explosion 41.6967509 41.1838791
## Facility/Infrastructure Attack 3.4296029 24.9370277
## Hijacking 1.4440433 2.2670025
## Hostage Taking (Barricade Incident) 0.3610108 0.3778338
## Hostage Taking (Kidnapping) 8.1227437 1.7632242
## Unarmed Assault 0.7220217 5.2896725
## Unknown 2.8880866 2.8967254
## terr.Region
## terr.attack Eastern Europe
## Armed Assault 24.9254621
## Assassination 7.6724309
## Bombing/Explosion 54.0647983
## Facility/Infrastructure Attack 4.7505466
## Hijacking 0.5167959
## Hostage Taking (Barricade Incident) 0.3975353
## Hostage Taking (Kidnapping) 4.2933810
## Unarmed Assault 1.1329756
## Unknown 2.2460743
## terr.Region
## terr.attack Middle East & North Africa
## Armed Assault 18.7912537
## Assassination 8.7291178
## Bombing/Explosion 60.8759218
## Facility/Infrastructure Attack 2.2553804
## Hijacking 0.2709037
## Hostage Taking (Barricade Incident) 0.1870525
## Hostage Taking (Kidnapping) 5.1966202
## Unarmed Assault 0.3526048
## Unknown 3.3411451
## terr.Region
## terr.attack North America South America
## Armed Assault 12.1338912 20.3816224
## Assassination 7.0830843 14.4600789
## Bombing/Explosion 45.6664674 47.8467114
## Facility/Infrastructure Attack 26.0609683 4.1360196
## Hijacking 0.5379558 0.3517749
## Hostage Taking (Barricade Incident) 1.8828452 1.2205522
## Hostage Taking (Kidnapping) 3.6162582 7.3446328
## Unarmed Assault 2.0621638 0.2505063
## Unknown 0.9563658 4.0081015
## terr.Region
## terr.attack South Asia Southeast Asia
## Armed Assault 25.5536545 32.6202742
## Assassination 9.5597272 10.8006636
## Bombing/Explosion 47.7962262 39.0814634
## Facility/Infrastructure Attack 4.5738246 7.3168602
## Hijacking 0.2048341 0.3754475
## Hostage Taking (Barricade Incident) 0.2409813 0.4103728
## Hostage Taking (Kidnapping) 7.2342579 5.8412643
## Unarmed Assault 0.6723378 0.2008207
## Unknown 4.1641564 3.3528333
## terr.Region
## terr.attack Sub-Saharan Africa Western Europe
## Armed Assault 34.6265574 10.1612804
## Assassination 9.3021755 17.8389649
## Bombing/Explosion 31.8249306 51.6587968
## Facility/Infrastructure Attack 4.5897618 15.4105599
## Hijacking 0.7488219 0.3863372
## Hostage Taking (Barricade Incident) 0.4712414 0.5273809
## Hostage Taking (Kidnapping) 10.2640243 1.6495983
## Unarmed Assault 0.4712414 0.7726743
## Unknown 7.7012459 1.5944073
g <- ggplot(data=terr) +
geom_mosaic(aes(fill = attack, x = product(Region)))+
labs(x = "Attack type", y = "Proportion")+
coord_flip()+
theme(legend.position = "none", panel.background = NULL, axis.text.y = element_text(angle=50, vjust=1))
#g
ggplotly(g, height = 500)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
Middle East & North Africa have higher proportion of bombings/explosions(60%) than any other region, followed by Eastern Europe(54%). Central America and Caribbean have a high percentage of armed assaults(42%).
#getting proportions across attack and region
t = table(data.frame(terr$attack,terr$target));
prop.table(t,2)*100## terr.target
## terr.attack Abortion Related Airports & Aircraft
## Armed Assault 3.04182510 7.65765766
## Assassination 3.42205323 1.20120120
## Bombing/Explosion 19.01140684 58.40840841
## Facility/Infrastructure Attack 73.00380228 7.05705706
## Hijacking 0.00000000 19.81981982
## Hostage Taking (Barricade Incident) 0.38022814 1.05105105
## Hostage Taking (Kidnapping) 0.38022814 1.35135135
## Unarmed Assault 0.76045627 0.45045045
## Unknown 0.00000000 3.00300300
## terr.target
## terr.attack Business Educational Institution
## Armed Assault 11.51310824 13.75000000
## Assassination 4.81557893 8.53365385
## Bombing/Explosion 58.63734715 51.85096154
## Facility/Infrastructure Attack 13.95360539 13.94230769
## Hijacking 0.30191717 0.21634615
## Hostage Taking (Barricade Incident) 0.88059176 0.76923077
## Hostage Taking (Kidnapping) 7.50767373 7.76442308
## Unarmed Assault 0.28178936 1.70673077
## Unknown 2.10838827 1.46634615
## terr.target
## terr.attack Food or Water Supply
## Armed Assault 13.81578947
## Assassination 0.00000000
## Bombing/Explosion 66.77631579
## Facility/Infrastructure Attack 11.84210526
## Hijacking 1.31578947
## Hostage Taking (Barricade Incident) 0.65789474
## Hostage Taking (Kidnapping) 2.30263158
## Unarmed Assault 1.64473684
## Unknown 1.64473684
## terr.target
## terr.attack Government (Diplomatic)
## Armed Assault 17.66939252
## Assassination 10.04672897
## Bombing/Explosion 47.80957944
## Facility/Infrastructure Attack 8.32359813
## Hijacking 0.67172897
## Hostage Taking (Barricade Incident) 1.66471963
## Hostage Taking (Kidnapping) 9.11214953
## Unarmed Assault 0.99299065
## Unknown 3.70911215
## terr.target
## terr.attack Government (General)
## Armed Assault 16.23510879
## Assassination 27.65580388
## Bombing/Explosion 39.30786650
## Facility/Infrastructure Attack 6.45367727
## Hijacking 0.06399527
## Hostage Taking (Barricade Incident) 0.32489908
## Hostage Taking (Kidnapping) 7.42345181
## Unarmed Assault 0.56118933
## Unknown 1.97400807
## terr.target
## terr.attack Journalists & Media Maritime
## Armed Assault 13.32858161 22.48520710
## Assassination 24.41197434 3.25443787
## Bombing/Explosion 26.44333571 39.05325444
## Facility/Infrastructure Attack 6.87811832 3.25443787
## Hijacking 0.10691376 14.20118343
## Hostage Taking (Barricade Incident) 8.33927299 0.59171598
## Hostage Taking (Kidnapping) 16.00142552 12.72189349
## Unarmed Assault 1.56806842 0.00000000
## Unknown 2.92230934 4.43786982
## terr.target
## terr.attack Military NGO Other
## Armed Assault 40.51670064 23.46491228 28.98550725
## Assassination 6.06476400 8.77192982 0.80515298
## Bombing/Explosion 42.15148189 23.13596491 36.07085346
## Facility/Infrastructure Attack 1.19178297 7.34649123 17.39130435
## Hijacking 0.08232711 0.54824561 0.48309179
## Hostage Taking (Barricade Incident) 0.10976948 0.65789474 0.64412238
## Hostage Taking (Kidnapping) 2.52077780 31.79824561 9.17874396
## Unarmed Assault 0.31362710 0.87719298 2.09339775
## Unknown 7.04876901 3.39912281 4.34782609
## terr.target
## terr.attack Police
## Armed Assault 36.14090156
## Assassination 12.06731188
## Bombing/Explosion 41.57729532
## Facility/Infrastructure Attack 2.00540588
## Hijacking 0.06103409
## Hostage Taking (Barricade Incident) 0.20054059
## Hostage Taking (Kidnapping) 2.85552359
## Unarmed Assault 0.33132793
## Unknown 4.76065917
## terr.target
## terr.attack Private Citizens & Property
## Armed Assault 25.22878432
## Assassination 10.14902235
## Bombing/Explosion 46.77701655
## Facility/Infrastructure Attack 3.28049207
## Hijacking 0.13752063
## Hostage Taking (Barricade Incident) 0.31504726
## Hostage Taking (Kidnapping) 9.18887833
## Unarmed Assault 0.75011252
## Unknown 4.17312597
## terr.target
## terr.attack Religious Figures/Institutions
## Armed Assault 18.88994759
## Assassination 10.60028585
## Bombing/Explosion 47.87994283
## Facility/Infrastructure Attack 13.62553597
## Hijacking 0.02382087
## Hostage Taking (Barricade Incident) 0.52405908
## Hostage Taking (Kidnapping) 5.59790376
## Unarmed Assault 0.66698428
## Unknown 2.19151977
## terr.target
## terr.attack Telecommunication
## Armed Assault 5.29531568
## Assassination 0.40733198
## Bombing/Explosion 60.59063136
## Facility/Infrastructure Attack 27.08757637
## Hijacking 0.00000000
## Hostage Taking (Barricade Incident) 3.66598778
## Hostage Taking (Kidnapping) 1.73116090
## Unarmed Assault 0.20366599
## Unknown 1.01832994
## terr.target
## terr.attack Terrorists/Non-State Militia
## Armed Assault 24.69394893
## Assassination 22.10563134
## Bombing/Explosion 40.15389997
## Facility/Infrastructure Attack 0.94438615
## Hijacking 0.00000000
## Hostage Taking (Barricade Incident) 0.10493179
## Hostage Taking (Kidnapping) 5.66631689
## Unarmed Assault 0.13990906
## Unknown 6.19097587
## terr.target
## terr.attack Tourists Transportation
## Armed Assault 20.51282051 21.28586450
## Assassination 8.39160839 0.87126333
## Bombing/Explosion 41.02564103 63.94772420
## Facility/Infrastructure Attack 3.03030303 8.74267688
## Hijacking 0.69930070 0.99143758
## Hostage Taking (Barricade Incident) 1.16550117 0.43563167
## Hostage Taking (Kidnapping) 22.84382284 1.12663362
## Unarmed Assault 0.93240093 0.66095839
## Unknown 1.39860140 1.93780982
## terr.target
## terr.attack Unknown Utilities
## Armed Assault 3.18079212 1.96648427
## Assassination 4.28893905 0.06839945
## Bombing/Explosion 89.32895547 90.80027360
## Facility/Infrastructure Attack 0.69772214 4.78796170
## Hijacking 0.10260620 0.01709986
## Hostage Taking (Barricade Incident) 0.04104248 0.18809850
## Hostage Taking (Kidnapping) 0.73876462 0.42749658
## Unarmed Assault 0.24625487 0.00000000
## Unknown 1.37492305 1.74418605
## terr.target
## terr.attack Violent Political Party
## Armed Assault 24.63599301
## Assassination 32.61502621
## Bombing/Explosion 26.49970879
## Facility/Infrastructure Attack 4.71753058
## Hijacking 0.00000000
## Hostage Taking (Barricade Incident) 0.05824112
## Hostage Taking (Kidnapping) 6.52300524
## Unarmed Assault 0.58241118
## Unknown 4.36808387
g <- ggplot(data=terr) +
geom_mosaic(aes(fill = attack, x = product(target)))+
labs(x = "Attack type", y = "Proportion")+
coord_flip()+
theme(legend.position = "none", panel.background = NULL, axis.text.y = element_blank())#element_text(angle=50, vjust=1))
#g
ggplotly(g, height = 500)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
Targets like utilities, transportation, ans business are mostly attacked by bombings or explosions.
#getting proportions across attack and region
attacks_known_ntnlty <- terr %>%
filter(INT_ANY != -9)
t = table(data.frame(attacks_known_ntnlty$INT_ANY,attacks_known_ntnlty$Region));
prop.table(t,2)*100## attacks_known_ntnlty.Region
## attacks_known_ntnlty.INT_ANY Australasia & Oceania
## 0 28.22581
## 1 71.77419
## attacks_known_ntnlty.Region
## attacks_known_ntnlty.INT_ANY Central America & Caribbean Central Asia
## 0 89.31891 28.20513
## 1 10.68109 71.79487
## attacks_known_ntnlty.Region
## attacks_known_ntnlty.INT_ANY East Asia Eastern Europe
## 0 64.34783 24.68293
## 1 35.65217 75.31707
## attacks_known_ntnlty.Region
## attacks_known_ntnlty.INT_ANY Middle East & North Africa North America
## 0 43.70093 50.87918
## 1 56.29907 49.12082
## attacks_known_ntnlty.Region
## attacks_known_ntnlty.INT_ANY South America South Asia Southeast Asia
## 0 88.90164 79.72249 87.02144
## 1 11.09836 20.27751 12.97856
## attacks_known_ntnlty.Region
## attacks_known_ntnlty.INT_ANY Sub-Saharan Africa Western Europe
## 0 62.86338 18.48627
## 1 37.13662 81.51373
g <- ggplot(data=attacks_known_ntnlty) +
geom_mosaic(aes(fill = factor(INT_ANY), x = product(Region)))+
labs(x = "Region", y = "Proportion")+
coord_flip()+
theme(legend.position = "none", panel.background = NULL, axis.text.y = element_blank())#element_text(angle=50, vjust=1))
#g
ggplotly(g)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
Western Europe had a fairly high number of international attacks(81%) as compared to domestic attacks, followed by Middle East & North Africa(56%). More than 87% of terrorist attacks in Central America & Caribbean Central, South America, and Southeast Asia were domestic.
#getting proportions across attack and region
most_attacked <- global_n %>%
top_n(10, Total)
most_attacked_nations <- terr %>%
filter(nation %in% most_attacked$nation & INT_ANY!=-9)
t = table(data.frame(most_attacked_nations$INT_ANY,most_attacked_nations$nation));
prop.table(t,2)*100## most_attacked_nations.nation
## most_attacked_nations.INT_ANY Afghanistan Colombia El Salvador
## 0 73.771340 91.878634 96.179183
## 1 26.228660 8.121366 3.820817
## most_attacked_nations.nation
## most_attacked_nations.INT_ANY India Iraq Pakistan Peru
## 0 84.775549 18.645027 79.741120 94.268128
## 1 15.224451 81.354973 20.258880 5.731872
## most_attacked_nations.nation
## most_attacked_nations.INT_ANY Philippines Turkey UK
## 0 92.719056 85.027125 0.000000
## 1 7.280944 14.972875 100.000000
g <- ggplot(data=most_attacked_nations) +
geom_mosaic(aes(fill = factor(INT_ANY), x = product(nation)))+
labs(x = "Region", y = "Proportion")+
coord_flip()+
theme(legend.position = "none", panel.background = NULL, axis.text.y = element_blank())#element_text(angle=50, vjust=1))
ggplotly(g)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
When looking at the attacks of the top 10 most attacked countries, we are looking at the attacks whose ideological or logistical nationality we know. All of the attacks in UK are international. After that, 81% of attacks in Iraq are logistically or ideologically international. More than 96% of the attacks in El Salvador, and more than 90% of the attacks in Columbia, Peru and Phillipines, are domestic.